Several estimators from the social science toolkit might be used to model the relationship between imprecisely-observed ranks in a hierarchy, and covariates explaining those ranks. But application of standard methods–such as linear regression, ordered probit, or censored regression–is complicated by the interdependence of rank observations. Monte Carlo evidence shows that estimators which either delete partially observed ranks and/or inappropriately assume ranks are iid perform poorly, yielding inefficient and sometimes biased estimates, and wildly inaccurate confidence intervals. In contrast, a Bayesian partial rank model–designed to impute missing ranks within known bounds, and account for interdependence across ranks–performs well, even when most or all ranks are observed imprecisely.

Submitted May 12, 2005 to the Superior Court of Chelan County, Washington

Expert witness report on ecological inference and the statistical analysis of elections in Borders v. King County, which contested the 2004 Washington gubernatorial election. The final (unappealed) ruling in favor of Governor Christine Gregoire can be found here. Wikipedia's entry on the 2004 Washington election can be found here, though I cannot vouch for the validity of wiki content.

Political scientists often and increasingly analyze time-series cross-sectional (tscs) data. These data come with significant problems, such as accounting for unobserved variation across sample units and appropriately specifying dynamics. Furthermore, even though fixed-effects (or least squares dummy variable (lsdv) models) can address unit heterogeneity, least squares (ls) estimation of modelds with fixed-effects and lagged dependent variables are known to be biased. Alternative estimators, mostly from economics, and generally designed for short panels, have been proposed to address this bias, but it is generally not well known how these estimators perform in comparison to simple methods like ls and lsdv on tscs data. The preliminary results we illustrate here suggest that lsdv is generally as good or better than instrumental variables (iv) approaches in terms of bias and efficiency. We examine estimator performance under conditions where the importance of the unit effects and the correlation of the unit effects with the independent variables are allowed to vary and find that lsdv performs well. Unfortunately none of the estimators, particularly ls, perform well when the dynamics of the model are mis-specified. The lesson is that new estimators do not, in general, solve the problem of mis-specifying the model’s dynamics.

In recent years, homeowner associations (hoas) in Harris County, Texas have ﬁled thousands of lawsuits threatening foreclosure against residents who owed dues, late fees, or ﬁnes. An event count analysis of hoa foreclosures by neighborhood from 1985—2001 shows the bulk of these ﬁlings occur in neighborhoods with low median home values. Overall, homeowners in the bottom quartile of home value face more than ten times the risk of hoa foreclosure proceedings as those in the top quartile. Legal changes in 1987 and 1995 also seem to have encouraged hoas to bring more foreclosures to court: across the spectrum of home values, the annual pace of ﬁling after 1995 is roughly double the previous decade’s rate. Although hoa foreclosures are ostensibly motivated by efforts to improve property values, neither foreclosure activity nor hoas appear linked with above average home price growth.

Replication: Data are drawn from HOAdata, which should be consulted for more recent data.